Cargando…
Predicting floods with Flickr tags
Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of d...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325575/ https://www.ncbi.nlm.nih.gov/pubmed/28235035 http://dx.doi.org/10.1371/journal.pone.0172870 |
_version_ | 1782510414872969216 |
---|---|
author | Tkachenko, Nataliya Jarvis, Stephen Procter, Rob |
author_facet | Tkachenko, Nataliya Jarvis, Stephen Procter, Rob |
author_sort | Tkachenko, Nataliya |
collection | PubMed |
description | Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak. |
format | Online Article Text |
id | pubmed-5325575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-53255752017-03-09 Predicting floods with Flickr tags Tkachenko, Nataliya Jarvis, Stephen Procter, Rob PLoS One Research Article Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak. Public Library of Science 2017-02-24 /pmc/articles/PMC5325575/ /pubmed/28235035 http://dx.doi.org/10.1371/journal.pone.0172870 Text en © 2017 Tkachenko et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tkachenko, Nataliya Jarvis, Stephen Procter, Rob Predicting floods with Flickr tags |
title | Predicting floods with Flickr tags |
title_full | Predicting floods with Flickr tags |
title_fullStr | Predicting floods with Flickr tags |
title_full_unstemmed | Predicting floods with Flickr tags |
title_short | Predicting floods with Flickr tags |
title_sort | predicting floods with flickr tags |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325575/ https://www.ncbi.nlm.nih.gov/pubmed/28235035 http://dx.doi.org/10.1371/journal.pone.0172870 |
work_keys_str_mv | AT tkachenkonataliya predictingfloodswithflickrtags AT jarvisstephen predictingfloodswithflickrtags AT procterrob predictingfloodswithflickrtags |